16 citations · 36 across the 21 of their papers we have counts for
21 papers
CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation
Tingyu Song, Mingxin Li, Yanzhao Zhang +5
MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different attribute-object bindings. Yet…
UEmbed: Unified Sparse and Dense Multimodal Embeddings
Tingyu Song, Mingxin Li, Yanzhao Zhang +5
Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exac…
CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding
Fuwei Zhang, Yanzhao Zhang, Mingxin Li +5
Code retrieval is becoming central to coding agents, but agentic coding requires more than matching a natural-language query to an isolated snippet. Given a user request, a coding…
LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
Jiajie Jin, Yanzhao Zhang, Mingxin Li +4
LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLM…
DiffuRank: Effective Document Reranking with Diffusion Language Models
Qi Liu, Kun Ai, Jiaxin Mao +6
Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the reasoning and contextual understanding c…
Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing
Tingyu Song, Yanzhao Zhang, Mingxin Li +6
Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the…
Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
Mingxin Li, Yanzhao Zhang, Dingkun Long +9
In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, t…
Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum
Zhuoning Guo, Mingxin Li, Yanzhao Zhang +3
The prevailing video retrieval paradigm is structurally misaligned, as narrow benchmarks incentivize correspondingly limited data and single-task training. Therefore, universal cap…
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li +6
In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant q…
E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search
Qi Liu, Yanzhao Zhang, Mingxin Li +3
Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-based listwise rerankers, which cap…
ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Yuzheng Cai, Yanzhao Zhang, Dingkun Long +3
Text reranking models are a crucial component in modern systems like Retrieval-Augmented Generation, tasked with selecting the most relevant documents prior to generation. However,…
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Yanzhao Zhang, Mingxin Li, Dingkun Long +9
In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon…
Towards Text-Image Interleaved Retrieval
Xin Zhang, Ziqi Dai, Yongqi Li +7
Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved cont…
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Xin Zhang, Yanzhao Zhang, Wen Xie +7
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a comb…
Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging
Mingxin Li, Zhijie Nie, Yanzhao Zhang +3
Text embeddings are vital for tasks such as text retrieval and semantic textual similarity (STS). Recently, the advent of pretrained language models, along with unified benchmarks…
An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
Peiming Guo, Sinuo Liu, Yanzhao Zhang +4
Photo-Sharing Multi-modal dialogue generation requires a dialogue agent not only to generate text responses but also to share photos at the proper moment. Using image text caption…
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Xin Zhang, Yanzhao Zhang, Dingkun Long +10
We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder…
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Longhui Zhang, Dingkun Long, Meishan Zhang +3
Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on th…
Text Representation Distillation via Information Bottleneck Principle
Yanzhao Zhang, Dingkun Long, Zehan Li +1
Pre-trained language models (PLMs) have recently shown great success in text representation field. However, the high computational cost and high-dimensional representation of PLMs…
A Two-Stage Adaptation of Large Language Models for Text Ranking
Longhui Zhang, Yanzhao Zhang, Dingkun Long +3
Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities…
Language Models are Universal Embedders
Xin Zhang, Zehan Li, Yanzhao Zhang +4
In the large language model (LLM) revolution, embedding is a key component of various systems, such as retrieving knowledge or memories for LLMs or building content moderation filt…